Aan de slag met decomposition trees

Laten we teruggaan naar de Glassdoor-gegevensset. In de vorige oefeningen heb je verbanden ontdekt tussen MonthlyIncome en andere variabelen in de gegevensset, zoals JobRole, JobLevel en CareerStage. Deze kun je gebruiken in een decomposition tree om sneller te analyseren hoe waarden binnen elke variabele de waarde van MonthlyIncome beïnvloeden.

In deze oefening doe je precies dat, zodat je begrijpt hoe de gemiddelde MonthlyIncome wordt beïnvloed door verschillende waarden van meerdere "explain by"-variabelen.

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In this chapter, you’ll get more familiar with time-based variables and the multiple ways to extract further variables using EDA for analysis—like day of week and time difference. You’ll get hands-on with Power BI as you build line charts to calculate new metrics and uncover trends hiding in your data—including period-over-period change and rolling averages.

Exercise 1: Starting to explore time series data Exercise 2: Statements about time series analysis Exercise 3: Exploring AirBnB time series Exercise 4: Generating a new date variable Exercise 5: Analyzing trends by day of week Exercise 6: Calculating year-over-year change

Exercise 1: Time series run chart Exercise 2: Cyclical vs. Seasonal Exercise 3: Analyzing Glassdoor reviews over time Exercise 4: Building your first time series chart Exercise 5: Calculating rolling averages Exercise 6: Finding anomalies in time series data

One of the most powerful functions of EDA in Power BI is being able to identify which variables have the most influence on your target outcome. A native Power BI visualization tool enabling that is Decomposition Trees. You'll learn about Decomposition Trees, how to construct, then interpret in order to explain a target outcome by other variables.

Exercise 1: Introductie tot decomposition trees Exercise 2: Uitspraken over decomposition trees Exercise 3: Decomposition trees gebruiken in Power BI Exercise 4: Aan de slag met decomposition trees

Huidige oefening

Exercise 5: Dieper duiken in decomposition trees Exercise 6: Attritionpercentages ontleden

In this chapter you'll build another native Power BI tool, Key Influencers visual. It helps you to understand how much a target outcome changes based on specific variables and segments of observations.

Exercise 1: Introduction to key influencers Exercise 2: Decomposition trees vs. key influencers Exercise 3: Using key influencers in Power BI Exercise 4: Key influencers for average listing price Exercise 5: Segments from key influencers Exercise 6: Key influencers for super hosts Exercise 7: Congratulations!